Project 1: History of Philosophy

library(beeswarm)
library(data.table)
library(dichromat)
library(dplyr)
library(factoextra)
library(fmsb)
library(ggplot2)
library(gridExtra)
library(qdap)
library(RANN)
library(RColorBrewer)
library(readtext)
library(rvest)
library(scales)
library(sentimentr)
library(stopwords)
library(stringr)
library(syuzhet)
library(textreuse)
library(tibble)
library(tidytext)
library(tidyverse)
library(tm)
library(topicmodels)
library(wesanderson)
library(wordcloud)
source("../lib/plot_stacked.R")
source("../lib/speech_funcs.R")
source("../lib/helper_funcs.R")

# df <- read.csv("../data/philosophy_data.csv")

# Read data using data.table to decrease runtime
df <- data.table::fread("../output/philosophy_data_table.csv")

df <- df %>%
  group_by(school) %>%
  mutate(mode_date = find_mode(original_publication_date)) %>%
  ungroup() %>%
  mutate(school = ifelse(school == "german_idealism", "German Idealism", str_to_title(school)),
         school_ordered = reorder(factor(school), mode_date, mean, order = TRUE))

palette <- get_palette(length(unique(df$school)))

Overview

par(mfrow = c(4, 4))
i <- 0
for(s in levels(df$school_ordered)) {
  docs <- Corpus(VectorSource(df[df$school == s, ]$sentence_str)) %>%
    tm_map(tolower) %>%
    tm_map(removePunctuation) %>%
    tm_map(removeNumbers) %>%
    tm_map(removeWords, stopwords("english")) %>%
    tm_map(stripWhitespace)
  
  tdm <- TermDocumentMatrix(docs)
  tdm_tidy <- tidy(tdm)
  tdm_overall <- summarise(group_by(tdm_tidy, term), sum(count))
  
  i <- i + 1
  plt_title <- s
  wordcloud(tdm_overall$term, tdm_overall$`sum(count)`,
            scale = c(5, 0.5),
            max.words = 200,
            min.freq = 1,
            random.order = FALSE,
            rot.per = 0.3,
            random.color = FALSE,
            colors = palette[i])
  mtext(plt_title, side = 3)
}

df %>%
  unnest_tokens(bigram, sentence_str, token = "ngrams", n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>%
  filter(!word1 %in% stopwords::stopwords(source = "snowball")) %>%
  filter(!word2 %in% stopwords::stopwords(source = "snowball")) %>%
  filter(!is.na(word1)) %>%
  filter(!is.na(word2)) %>%
  unite(bigram, word1, word2, sep = " ") %>%
  group_by(school_ordered, bigram) %>%
  summarize(n_bigram = n()) %>%
  slice_max(n_bigram, n = 10) %>%
  mutate(bigram = reorder_within(bigram, n_bigram, school_ordered)) %>%
  ggplot(aes(x = bigram, y = n_bigram, fill = school_ordered)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  facet_wrap(~school_ordered, scales = "free") +
  coord_flip() +
  scale_x_reordered() +
  scale_fill_manual(values = get_palette(length(unique(df$school)))) +
  labs(title = "Top 10 Bigrams by School", x = element_blank(), y = "Count") +
  theme_classic()

Philosophy speech

df %>%
  ggplot(aes(x = sentence_length, fill = school_ordered)) +
  geom_histogram(binwidth = 2, size = 0.25, show.legend = FALSE) +
  xlim(0, 500) +
  facet_grid(rows = vars(school_ordered)) +
  scale_fill_manual(values = palette) +
  labs(title = "Sentence Length by School", x = "Word count", y = "Sentence count") +
  theme_classic()

Sentiment analysis

emotions <- data.table::fread("../output/emotions.csv")
df_emotions <- cbind(df, emotions)

par(mfrow = c(4, 4))
i <- 0
for(s in levels(df$school_ordered)) {
  emotions_by_school <- df_emotions %>%
    mutate(school = ifelse(school == "german_idealism", "German Idealism", str_to_title(school))) %>%
    filter(school == s) %>%
    dplyr::select(anger, anticipation, disgust, fear, joy, sadness, surprise, trust) %>%
    bind_rows(summarise_all(., ~if(is.numeric(.)) sum(.) else "Total")) %>%
    slice_tail(n = 1) %>%
    as.data.frame()
  emotions_by_school <- rbind(rep(max(emotions_by_school), 8), rep(min(emotions_by_school), 8), emotions_by_school)
  
  i <- i + 1
  plt_title <- s
  radarchart(emotions_by_school, title = plt_title,
           pcol = palette[i], pfcol = alpha(palette[i], 0.3), plwd = 2,
           cglcol = "grey", cglty = 1, axislabcol = "grey", caxislabels = seq(0,20,5))
}

Similarity analysis